



'''simple docstring'''



import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
lowerCAmelCase_  :							List[str]             =    logging.get_logger('transformers.models.speecht5')
def        UpperCAmelCase (    A	:					int       ,		A	:					Any       ,		A	:					Tuple					):
							hf_model.apply_weight_norm()

							SCREAMING_SNAKE_CASE       :   Optional[Any]         =      checkpoint['''input_conv.weight_g''']
							SCREAMING_SNAKE_CASE       :   int         =      checkpoint['''input_conv.weight_v''']
							SCREAMING_SNAKE_CASE       :   Any         =      checkpoint['''input_conv.bias''']

							for i in range(len(config.upsample_rates					)					):
														SCREAMING_SNAKE_CASE       :   List[str]         =      checkpoint[F"""upsamples.{i}.1.weight_g"""]
														SCREAMING_SNAKE_CASE       :   Optional[Any]         =      checkpoint[F"""upsamples.{i}.1.weight_v"""]
														SCREAMING_SNAKE_CASE       :   Tuple         =      checkpoint[F"""upsamples.{i}.1.bias"""]

							for i in range(len(config.upsample_rates					) * len(config.resblock_kernel_sizes					)					):
														for j in range(len(config.resblock_dilation_sizes					)					):
																					SCREAMING_SNAKE_CASE       :   Optional[int]         =      checkpoint[F"""blocks.{i}.convs1.{j}.1.weight_g"""]
																					SCREAMING_SNAKE_CASE       :   Optional[Any]         =      checkpoint[F"""blocks.{i}.convs1.{j}.1.weight_v"""]
																					SCREAMING_SNAKE_CASE       :   Any         =      checkpoint[F"""blocks.{i}.convs1.{j}.1.bias"""]

																					SCREAMING_SNAKE_CASE       :   List[Any]         =      checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_g"""]
																					SCREAMING_SNAKE_CASE       :   List[Any]         =      checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_v"""]
																					SCREAMING_SNAKE_CASE       :   Optional[Any]         =      checkpoint[F"""blocks.{i}.convs2.{j}.1.bias"""]

							SCREAMING_SNAKE_CASE       :   Tuple         =      checkpoint['''output_conv.1.weight_g''']
							SCREAMING_SNAKE_CASE       :   Any         =      checkpoint['''output_conv.1.weight_v''']
							SCREAMING_SNAKE_CASE       :   int         =      checkpoint['''output_conv.1.bias''']

							hf_model.remove_weight_norm()
@torch.no_grad()
def        UpperCAmelCase (    A	:					Tuple       ,		A	:					List[str]       ,		A	:					str       ,		A	:					Any=None       ,		A	:					int=None       ,		):
							if config_path is not None:
														SCREAMING_SNAKE_CASE       :   Optional[int]         =      SpeechTaHifiGanConfig.from_pretrained(A					)
							else:
														SCREAMING_SNAKE_CASE       :   Any         =      SpeechTaHifiGanConfig()

							SCREAMING_SNAKE_CASE       :   Any         =      SpeechTaHifiGan(A					)

							SCREAMING_SNAKE_CASE       :   List[str]         =      torch.load(A					)
							load_weights(orig_checkpoint['''model''']['''generator''']       ,		A       ,		A					)

							SCREAMING_SNAKE_CASE       :   Dict         =      np.load(A					)
							SCREAMING_SNAKE_CASE       :   Optional[int]         =      stats[0].reshape(-1					)
							SCREAMING_SNAKE_CASE       :   Tuple         =      stats[1].reshape(-1					)
							SCREAMING_SNAKE_CASE       :   Any         =      torch.from_numpy(A					).float()
							SCREAMING_SNAKE_CASE       :   List[Any]         =      torch.from_numpy(A					).float()

							model.save_pretrained(A					)

							if repo_id:
														print('''Pushing to the hub...'''					)
														model.push_to_hub(A					)


if __name__ == "__main__":
							lowerCAmelCase_  :							Any             =    argparse.ArgumentParser()
							parser.add_argument('--checkpoint_path', required=True, default=None, type=str, help='Path to original checkpoint')
							parser.add_argument('--stats_path', required=True, default=None, type=str, help='Path to stats.npy file')
							parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
							parser.add_argument(
							    '--pytorch_dump_folder_path', required=True, default=None, type=str, help='Path to the output PyTorch model.'
							)
							parser.add_argument(
							    '--push_to_hub', default=None, type=str, help='Where to upload the converted model on the 🤗 hub.'
							)

							lowerCAmelCase_  :							Any             =    parser.parse_args()
							convert_hifigan_checkpoint(
							    args.checkpoint_path,
							    args.stats_path,
							    args.pytorch_dump_folder_path,
							    args.config_path,
							    args.push_to_hub,
							)









'''simple docstring'''



import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()
def        UpperCAmelCase (    A	:					Optional[Any]       ,		A	:					Optional[int]       ,		A	:					int					):
							# Initialise PyTorch model
							SCREAMING_SNAKE_CASE       :   str         =      TaConfig.from_json_file(A					)
							print(F"""Building PyTorch model from configuration: {config}"""					)
							SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      TaForConditionalGeneration(A					)

							# Load weights from tf checkpoint
							load_tf_weights_in_ta(A       ,		A       ,		A					)

							# Save pytorch-model
							print(F"""Save PyTorch model to {pytorch_dump_path}"""					)
							model.save_pretrained(A					)


if __name__ == "__main__":
							lowerCAmelCase_  :							Union[str, Any]             =    argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
							)
							parser.add_argument(
							    '--config_file',
							    default=None,
							    type=str,
							    required=True,
							    help=(
							        'The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture.'
							    ),
							)
							parser.add_argument(
							    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
							)
							lowerCAmelCase_  :							Tuple             =    parser.parse_args()
							convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)




